A Cover-Independent Deep Image Hiding Method Based on Domain Attention Mechanism

Recently,deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information hiding.However,these approaches have some limitations.For example,a cover image lacks self-adaptability,information leakage,or weak concealment.To address these issues,this study proposes a universal and adaptable image-hiding method.First,a domain attention mechanism is designed by combining the Atrous convolution,which makes better use of the relationship between the secret image domain and the cover image domain.Second,to improve perceived human similarity,perceptual loss is incorporated into the training process.The experimental results are promising,with the proposed method achieving an average pixel discrepancy(APD)of 1.83 and a peak signal-to-noise ratio(PSNR)value of 40.72 dB between the cover and stego images,indicative of its high-quality output.Furthermore,the structural similarity index measure(SSIM)reaches 0.985 while the learned perceptual image patch similarity(LPIPS)remarkably registers at 0.0001.Moreover,self-testing and cross-experiments demonstrate the model’s adaptability and generalization in unknown hidden spaces,making it suitable for diverse computer vision tasks.

Robust Deep Image Watermarking:A Survey

In the era of internet proliferation,safeguarding digital media copyright and integrity,especially for images,is imperative.Digital watermarking stands out as a pivotal solution for image security.With the advent of deep learning,watermarking has seen significant advancements.Our review focuses on the innovative deep watermarking approaches that employ neural networks to identify robust embedding spaces,resilient to various attacks.These methods,characterized by a streamlined encoder-decoder architecture,have shown enhanced performance through the incorporation of novel training modules.This article offers an in-depth analysis of deep watermarking’s core technologies,current status,and prospective trajectories,evaluating recent scholarly contributions across diverse frameworks.It concludes with an overview of the technical hurdles and prospects,providing essential insights for ongoing and future research endeavors in digital image watermarking.

Stimulated Raman scattering microscopy with phase-controlled light focusing and aberration correction for rapid and label-free, volumetric deep tissue imaging

We report a novel stimulated Raman scattering(SRS)microscopy technique featuring phase-controlled light focusing and aberration corrections for rapid,deep tissue 3D chemical imaging with subcellular resolution.To accomplish phasecontrolled SRS(PC-SRS),we utilize a single spatial light modulator to electronically tune the axial positioning of both the shortened-length Bessel pump and the focused Gaussian Stokes beams,enabling z-scanning-free optical sectioning in the sample.By incorporating Zernike polynomials into the phase patterns,we simultaneously correct the system aberrations at two separate wavelengths(~240 nm difference),achieving a~3-fold enhancement in signal-to-noise ratio over the uncorrected imaging system.PC-SRS provides>2-fold improvement in imaging depth in various samples(e.g.,polystyrene bead phantoms,porcine brain tissue)as well as achieves SRS 3D imaging speed of~13 Hz per volume for real-time monitoring of Brownian motion of polymer beads in water,superior to conventional point-scanning SRS 3D imaging.We further utilize PC-SRS to observe the metabolic activities of the entire tumor liver in living zebrafish in cellsilent region,unraveling the upregulated metabolism in liver tumor compared to normal liver.This work shows that PCSRS provides unprecedented insights into morpho-chemistry,metabolic and dynamic functioning of live cells and tissue in real-time at the subcellular level.

Confocal rescan structured illumination microscopy for real-time deep tissue imaging with superresolution

Structured illumination microscopy(SIM)is an established optical superresolution imaging technique.However,conventional SIM based on wide-field image acquisition is generally limited to visualizing thin cellular samples.We propose combining one-dimensional image rescan and structured illumination in the orthogonal direction to achieve superresolution without the need to rotate the illumination pattern.The image acquisition speed is consequently improved threefold,which is also beneficial for minimizing photobleaching and phototoxicity.Optical sectioning in thick biological tissue is enhanced by including a confocal slit in the system to significantly suppress the out-of-focus background and the associated noise.With all the technical improvements,our method captures threedimensional superresolved image stacks of neuronal structures in mouse brain tissue samples for a depth range of more than 200μm.

Deep image prior plus sparsity prior:toward single-shot full-Stokes spectropolarimetric imaging with a multiple-order retarder

Compressive full-Stokes spectropolarimetric imaging(SPI),integrating passive polarization modulator(PM)into general imaging spectrometer,is powerful enough to capture high-dimensional information via incomplete measurement;a reconstruction algorithm is needed to recover 3D data cube(x,y,andλ)for each Stokes parameter.However,existing PMs usually consist of complex elements and enslave to accurate polarization calibration,current algorithms suffer from poor imaging quality and are subject to noise perturbation.In this work,we present a single multiple-order retarder followed a polarizer to implement passive spectropolarimetric modulation.After building a unified forward imaging model for SPI,we propose a deep image prior plus sparsity prior algorithm for high-quality reconstruction.The method based on untrained network does not need training data or accurate polarization calibration and can simultaneously reconstruct the 3D data cube and achieve self-calibration.Furthermore,we integrate the simplest PM into our miniature snapshot imaging spectrometer to form a single-shot SPI prototype.Both simulations and experiments verify the feasibility and outperformance of our SPI scheme.It provides a paradigm that allows general spectral imaging systems to become passive full-Stokes SPI systems by integrating the simplest PM without changing their intrinsic mechanism.

The Influences of American Deep Image on the Third-Generation Poets in China

The Third-Generation Poetry of China （namely Post-misty Poetry too） initiated with the introduction of Western modernist poetry, especially sorts of American Post-modernist poetry schools into China. ＂The relation between American poetry and Chinese poetry has a long history, which lies in the influences on the creation of the Third-Generation poets. This influence is probably unprecedented in its depth and breadth.＂ ＂Irrational association＂ and ＂leaping images＂ proposed by American Deep Image poets influenced by Freudian and Jungian unconscious perception gained an extraordinary appreciation among the Third-Generation poets who were in pursuit constantly of the experiments on poetic form and language. This paper mainly discusses the influences of American Deep Image on the Third-Generation poets of China through a case study of WANG Yin and CHEN Dongdong＇s poems.

B^(2)C^(3)NetF^(2):Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

Currently,the improvement in AI is mainly related to deep learning techniques that are employed for the classification,identification,and quantification of patterns in clinical images.The deep learning models show more remarkable performance than the traditional methods for medical image processing tasks,such as skin cancer,colorectal cancer,brain tumour,cardiac disease,Breast cancer(BrC),and a few more.The manual diagnosis of medical issues always requires an expert and is also expensive.Therefore,developing some computer diagnosis techniques based on deep learning is essential.Breast cancer is the most frequently diagnosed cancer in females with a rapidly growing percentage.It is estimated that patients with BrC will rise to 70%in the next 20 years.If diagnosed at a later stage,the survival rate of patients with BrC is shallow.Hence,early detection is essential,increasing the survival rate to 50%.A new framework for BrC classification is presented that utilises deep learning and feature optimization.The significant steps of the presented framework include(i)hybrid contrast enhancement of acquired images,(ii)data augmentation to facilitate better learning of the Convolutional Neural Network(CNN)model,(iii)a pre‐trained ResNet‐101 model is utilised and modified according to selected dataset classes,(iv)deep transfer learning based model training for feature extraction,(v)the fusion of features using the proposed highly corrected function‐controlled canonical correlation analysis approach,and(vi)optimal feature selection using the modified Satin Bowerbird Optimization controlled Newton Raphson algorithm that finally classified using 10 machine learning classifiers.The experiments of the proposed framework have been carried out using the most critical and publicly available dataset,such as CBISDDSM,and obtained the best accuracy of 94.5%along with improved computation time.The comparison depicts that the presented method surpasses the current state‐ofthe‐art approaches.

Design of axial flaps with color Doppler flow imaging technique for repairing deep wounds of heels

To report the methods and effect of axial pattern flap on lower limb in repairing deep wounds of heels by using color Doppler flow imaging (CDFI) technique so as to solve the ever before problems that the vessel can not be displayed in designing axial flap.Methods Suitable axial flaps on lower limbs were selected according to the character of the wounds.There were 25 flaps including 10 cases of the distal-based sural neurovascular flap,nine medial sole flap and six medial leg flap.All the axial pattern flaps were designed on the basis of traditional design ways before operation;then,CDFI appliance with high resolution was used to examine the starting spot,exterior diameter,trail and length of the flap’s major artery.The flaps were redesigned according to the results of CDFI and transferred to cover the wounds.In the meantime,both the results of operation and examination were compared.Results The major artery’s starting spot,exterior diameter,trail and anatomic layers were displayed clearly,in consistency with the results of operation.The flaps survived completely and recovered well,with perfect appearance,color and arthral function.Conclusion CDFI is a simple,macroscopic and atraumatic method for designing the axial pattern flap on lower limb,can provide more scientific and accurate evidence for preoperative determination of flap transplantation and is worthy of clinical application.10 refs,4 figs,2 tabs.

Classification of Domestic Refuse in Medical Institutions Based on Transfer Learning and Convolutional Neural Network

The problem of domestic refuse is becoming more and more serious with the use of all kinds of equipment in medical institutions.This matter arouses people’s attention.Traditional artificial waste classification is subjective and cannot be put accurately;moreover,the working environment of sorting is poor and the efficiency is low.Therefore,automated and effective sorting is needed.In view of the current development of deep learning,it can provide a good auxiliary role for classification and realize automatic classification.In this paper,the ResNet-50 convolutional neural network based on the transfer learning method is applied to design the image classifier to obtain the domestic refuse classification with high accuracy.By comparing the method designed in this paper with back propagation neural network and convolutional neural network,it is concluded that the CNN based on transfer learning method applied in this paper with higher accuracy rate and lower false detection rate.Further,under the shortage situation of data samples,the method with transfer learning and ResNet-50 training model is effective to improve the accuracy of image classification.

DSC based Dual-Resunet for radio frequency interference identification

Radio frequency interference(RFI)will pollute the weak astronomical signals received by radio telescopes,which in return will seriously affect the time-domain astronomical observation and research.In this paper,we use a deep learning method to identify RFI in frequency spectrum data,and propose a neural network based on Unet that combines the principles of depthwise separable convolution and residual,named DSC Based Dual-Resunet.Compared with the existing Unet network,DSC Based Dual-Resunet performs better in terms of accuracy,F1 score,and MIoU,and is also better in terms of computation cost where the model size and parameter amount are 12.5%of Unet and the amount of computation is 38%of Unet.The experimental results show that the proposed network is a high-performance and lightweight network,and it is hopeful to be applied to RFI identification of radio telescopes on a large scale.

Highly sensitive solar-blind deep ultraviolet photodetector based on graphene/PtSe_(2)/β-Ga_(2)O_(3)2D/3D Schottky junction with ultrafast speed

There is an emerging need for high-sensitivity solar-blind deep ultraviolet(DUV)photodetectors with an ultra-fast response speed.Although nanoscale devices based on Ga_(2)O_(3)nanostructures have been developed,their practical applications are greatly limited by their slow response speed as well as low specific detectivity.Here,the successful fabrication of two-/three-dimensional(2D/3D)graphene(Gr)/PtSe2/β-Ga_(2)O_(3)Schottky junction devices for high-sensitivity solar-blind DUV photodetectors is demonstrated.Benefitting from the high-quality 2D/3D Schottky junction,the vertically stacked structure,and the superior-quality transparent graphene electrode for effective carrier collection,the photodetector is highly sensitive to DUV light illumination and achieves a high responsivity of 76.2 mA/W,a large on/off current ratio of~105,along with an ultra-high ultraviolet(UV)/visible rejection ratio of 1.8×104.More importantly,it has an ultra-fast response time of 12µs and a remarkable specific detectivity of~1013 Jones.Finally,an excellent DUV imaging capability has been identified based on the Gr/PtSe2/β-Ga_(2)O_(3)Schottky junction photodetector,demonstrating its great potential application in DUV imaging systems.

Injectable and thermosensitive hydrogels mediating a universal macromolecular contrast agent with radiopacity for noninvasive imaging of deep tissues

It is very challenging to visualize implantable medical devices made of biodegradable polymers in deep tissues.Herein,we designed a novel macromolecular contrast agent with ultrahigh radiopacity(iodinate content>50%)via polymerizing an iodinated trimethylene carbonate monomer into the two ends of poly(ethylene glycol)(PEG).A set of thermosensitive and biodegradable polyester-PEG-polyester triblock copolymers with varied polyester compositions synthesized by us,which were soluble in water at room temperature and could spontaneously form hydrogels at body temperature,were selected as the demonstration materials.The addition of macromolecular contrast agent did not obviously compromise the injectability and thermogelation properties of polymeric hydrogels,but conferred them with excellent X-ray opacity,enabling visualization of the hydrogels at clinically relevant depths through X-ray fluoroscopy or Micro-CT.In a mouse model,the 3D morphology of the radiopaque hydrogels after injection into different target sites was visible using Micro-CT imaging,and their injection volume could be accurately obtained.Furthermore,the subcutaneous degradation process of a radiopaque hydrogel could be non-invasively monitored in a real-time and quantitative manner.In particular,the corrected degradation curve based on Micro-CT imaging well matched with the degradation profile of virgin polymer hydrogel determined by the gravimetric method.These findings indicate that the macromolecular contrast agent has good universality for the construction of various radiopaque polymer hydrogels,and can nondestructively trace and quantify their degradation in vivo.Meanwhile,the present methodology developed by us affords a platform technology for deep tissue imaging of polymeric materials.

Temperature-sensitive polymeric nanogels encapsulating withβ-cyclodextrin and ICG complex for high-resolution deep-tissue ultrasound-switchable fluorescence imaging

One of the thorny problems currently impeding the applications of the fluorescence imaging technique is the poor spatial resolution in deep tissue.Ultrasound-switchable fluorescence(USF)imaging is a novel imaging tool that has recently been explored to possibly surmount the above-mentioned bottleneck.Herein,αβ-cyclodextrin/indocyanine green(ICG)complex-encapsulated poly(N-isopropylacrylamide)(PNIPAM)nanogel was synthesized and studied for ex vivo/in vivo deep tissue/high-resolution near infrared USF(NIR-USF)imaging.To be specific,our results revealed that the average diameter of the as-prepared nanogels was significantly decreased to-32 nm from-335 nm compared to the reported ICG-PNIPAM nanoparticles.Additionally,the excitation/emission characteristics of the ICG itself in present nanogels were almost completely retained,and the resultant nanogel exhibited high physiological stability and positive biocompatibility.In particular,the signal-to-noise ratio of the USF image for the PNIPAM/P-cyclodextrin/ICG nanogel(33.01±2.42 dB)was prominently higher than that of the ICG-PNIPAM nanoparticles(18.73±0.33 dB)in 1.5-cm-thick chicken breast tissues.The NIR-USF imaging in 3.5-cm-thick chicken breast tissues was achieved using this new probe.The e x v iv o NIR-USF imaging of the mouse liver was also successfully obtained.Animal experiments showed that the present nanogels were able to be effectively accumulated into U87 tumor-bearing mice via enhanced permeability and retention effects,and the high-resolution NIR-USF imaging of in v ivo tumor was efficiently acquired.The metabolism and in vivo biodistribution of the nanogels were evaluated.Overall,the results suggest that the current nanogel is a highly promising NIR-USF probe for deep tissue and high-resolution USF imaging.

Reconstruction of 3D light distribution produced by cylindrical diffuser in deep tissues based on photoacoustic imaging

Measurement of light distribution in biological tissue contributes to selecting strategy and optimizing dose for biomedical application. In this letter, a photoacoustic method combined with Monte Carlo simulation was used to estimate the three-dimensional light distribution in biological tissue. The light distribution was produced by a cylindrical diffuser which interposed into tissues. The light profiles obtained by the method were compared to those detected by photo diodes. The experimental results demonstrate the feasibility of this method. The approach can play a significant role for photo-dosimetry in biomedical phototherapy.

GPU based techniques for deep image merging

Deep images store multiple fragments perpixel,each of which includes colour and depth,unlike traditional 2D flat images which store only a single colour value and possibly a depth value.Recently,deep images have found use in an increasing number of applications,including ones using transparency and compositing.A step in compositing deep images requires merging per-pixel fragment lists in depth order;little work has so far been presented on fast approaches.This paper explores GPU based merging of deep images using different memory layouts for fragment lists:linked lists,linearised arrays,and interleaved arrays.We also report performance improvements using techniques which leverage GPU memory hierarchy by processing blocks of fragment data using fast registers,following similar techniques used to improve performance of transparency rendering.We report results for compositing from two deep images or saving the resulting deep image before compositing,as well as for an iterated pairwise merge of multiple deep images.Our results show a 2 to 6 fold improvement by combining efficient memory layout with fast register based merging.

Stimulated Raman scattering tomography for rapid three-dimensional chemical imaging of cells and tissue

Three-dimensional(3D)imaging is essential for understanding intricate biological and biomedical systems,yet live cell and tissue imaging applications still face challenges due to constrained imaging speed and strong scattering in turbid media.Here,we present a unique phase-modulated stimulated Raman scattering tomography(PM-SRST)technique to achieve rapid label-free 3D chemical imaging in cells and tissue.To accomplish PM-SRST,we utilize a spatial light modulator to electronically manipulate the focused Stokes beam along the needle Bessel pump beam for SRS tomography without the need for mechanical z scanning.We demonstrate the rapid 3D imaging capability of PM-SRST by real-time monitoring of 3D Brownian motion of polystyrene beads in water with 8.5 Hz volume rate,as well as the instant biochemical responses to acetic acid stimulants in MCF-7 cells.Further,combining the Bessel pump beam with a longer wavelength Stokes beam(NIR-II window)provides a superior scattering resilient ability in PM-SRST,enabling rapid tomography in deeper tissue areas.The PM-SRST technique providestwofold enhancement in imaging depth in highly scattering media(e.g.,polymer beads phantom and biotissue like porcine skin and brain tissue)compared with conventional point-scan SRS.We also demonstrate the rapid 3D imaging ability of PM-SRST by observing the dynamic diffusion and uptake processes of deuterium oxide molecules into plant roots.The rapid PM-SRST developed can be used to facilitate label-free 3D chemical imaging of metabolic activities and functional dynamic processes of drug delivery and therapeutics in live cells and tissue.

Optical Brain Imaging： A Powerful Tool for Neuroscience

As the control center of organisms, the brain remains little understood due to its complexity. Taking advantage of imaging methods, scientists have found an accessible approach to unraveling the mystery of neuroscience. Among these methods, optical imaging techniques are widely used due to their high molecular specificity and single-molecule sensitivity. Here, we overview several optical imaging techniques in neuroscience of recent years, including brain clearing, the micro-optical sectioning tomography system, and deep tissue imaging.

In vivo tumor ultrasound-switchable fluorescence imaging via intravenous injections of size-controlled thermosensitive nanoparticles

Near-infrared fluorescence imaging has emerged as a noninvasive,inexpensive,and ionizing-radiation-free monitoring tool for assessing tumor growth and treatment efficacy.In particular,ultrasound switchable fluorescence(USF)imaging has been explored with improved imaging sensitivity and spatial resolution in centimeter-deep tissues.This study achieved the size control of polymer-based and indocyanine green(ICG)encapsulated USF contrast agents,capable of accumulating in the tumor after intravenous injections.These nanoprobes varied in size from 58 to 321 nm.The bioimaging profiles demonstrated that the proposed nanoparticles can efficiently eliminate the background light from normal tissue and show a tumor-specific fluorescence enhancement in the BxPC-3 tumor-bearing mice models possibly via the enhanced permeability and retention effect.In vivo tumor USF imaging further demonstrated that these nanoprobes can effectively be switched“ON”with enhanced fluorescence in response to a focused ultrasound stimulation in the tumor microenvironment,contributing to the high-resolution USF images.Therefore,our findings suggest that ICG-encapsulated nanoparticles are good candidates for USF imaging of tumors in live animals,indicating their great potential in optical tumor imaging in deep tissue.

Thermography analyses of rock fracture due to excavation and overloading for tunnel in 30° inclined strata

Large-scale physical model test of 30°inclined strata was conducted to investigate the damage mechanisms during the excavation and overloading using infrared detection.The experiment results were presented with thermal images which were divided into three stages including a full face excavation stage,a staged excavation stage,and an overloading stage.The obtained results were compared with the previously reported results from horizontal,45?,60?,and vertical strata models.Infrared temperature(IRT)for 30°inclined strata model descended with multiple fluctuations during the full-face excavation.For the staged excavation,the excavation damage zone(EDZ)showed enhanced faulting-like strips as compared in the 45?,60?,and vertical models,indicating the intensified stress redistribution occurred in the adjacent rock mass.In contrast,EDZ for the horizontal strata existed in a plastic-formed manner.During the overloading,abnormal features in the thermal images were observed preceding the coalescence of the propagating cracks.The ultimate failure of the model was due primarily to the floor heave and the roof fall.